AI Use Cases 

SignAll's solution provides every day assistance to deaf people by translating spoken English into Automatic Sign Language or vice-versa

Pregnancy management is a complicated challenge - especially where traditional support networks may no longer exist in the same way. Capturing health data potentially enables an algorithm to provide first line advisory support.

Using machine learning to predict readmission risk associated with each patient and suggest preventive care methods

Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU).

Modelling risk of long term ailment (e..g diabetes) development from relevant data. Data might include medical notes, real time sensor information, dietary or exercise feedback.

Optimise design of clinical trials, including label writing and patient selection

Predict risk of long term diagnosis of disease (e.g. heart attack in the next 10 years).

Identify target patient subgroups that are underserved or not diagnosed to support creation / deployment of a mitigation strategy

Automated equipment to assist patients and healthcare practitioners with physical mobility, supplemented by artificial intelligence. Often implemented in therapy and rehabilitation or assisted living facilities. Includes aritificial limbs, walkers, wheelchairs etc.

Monitoring and prompting prescription compliance. This will impact both on patient recovery rates but also potentially lowers the risk of folllow-on issues such as antibiotic drug resistance.

Using NLP automatically translate into text or sign language or describe surroundings

Predict individual hospital admission rates using historical and real-time data. The output will hemp both individuals and institions affected but also overall capacity planning across medical organisations.

Automated equipment to assist patients and healthcare practitioners with physical mobility, supplemented by artificial intelligence. Often implemented in therapy and rehabilitation or assisted living facilities. Includes artificial limbs, walkers, wheelchairs etc.

Using AI-powered robotics to enhance surgical operations. This will assist greater precision, reduces risks created by manual fatigue and ensures tight oversight and recall of the task in hand.

Analysing health insurance claims data to detect unusual patterns which may indicate fraud

Predicting drug demand in different geographies for different products. This will potentially be driven by factors from disease outbreak vectors through to socio-economic indicators or even media-driven consumption demand trends.

Reduce hospital-acquired infections (e.g. pneumonia, sepsis, urinary tract infection and surgical site infection) by identifying risk and suggesting appropriate responses.

Diagnose known diseases from scans, images, biopsies, predictive analytics audio, and other data

Early diagnosis can significantly increase chances of recovery and reduce harm, but over-hasty medication can be harmful for the individual and expensive for the supplier. Predicting likely outcomes from very early stage symptoms can potentially deliver better results for all.

Assisted or automated diagnosis and prescription helps patients in response to easily capturable symptoms, or existing conditions. Whilst useful in developed medical systems the potential in underserved areas of the world could be transformative.

Robo-advisor automates investment and portfolio rebalancing and transactions based on defined client needs, responding to real-time market data

Automate personal finance management (e.g. loan repayments by personal agents). This might be a consumer-downloadable app that syncs with their banking products to support achievement of personal financial goals. This might include automating savings for consumer by forecasting wallet availability based on customer spending habits and preferences. A typical use case deployment is a "rounding up" function on a digital payment - so a $3.63 payment triggers a round up to $4.00, automatically depositing 37 cents in a specified savings account.

Compile aggregate analyst report sentiment score based on the balance of positive to negative words in written reports

Track (sometimes in or near real-time) risk exposures, ensuring capital requirements monitored and overall business risk reduced.

Predict how events like natural disasters will affect market prices based on data on similar events

Use chatbot functionality to engage with customer and provide responses to key questions

Use geo-data gathered from multiple image sources (satellites, drones etc) and use Machine learning to assess potential agricultural outcomes. AI enables frequent model updates based on new risk assessments as they occur.

Enhance customer underwriting risk predictions through machine learning on multiple approaches and vectors

Automatically assess claim through accessing visuals of claimed damage. This might be ground or aerially sourced.

Use social media footprint to build predictive models for assessing individuals behaviour in the real world - specifically driving behaviour and therefore insurance risk. This raises potential data protection concerns in markets like Europe but is a key part of China's national AI development roll-out.